With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world. Unlike traditional AI, Physical AI operates under real-time safety constraints, continuously interacts with dynamic environments, and coexists with humans, introducing governance challenges that existing AI governance frameworks do not explicitly address. This paper presents a comprehensive survey of Physical AI governance from both scientific and operational perspectives. We synthesize existing governance principles and organize them into a unified governance framework tailored to physical AI systems. Building on this foundation, we propose a five-stage Physical AI lifecycle comprising research, design, data, model development, and deployment, and demonstrate how governance can be operationalized across each stage through concrete implementation practices. By connecting governance principles with engineering workflows, this survey provides a structured reference for researchers, developers, and policymakers to build Physical AI systems that are safe, trustworthy, and aligned with societal values.
Recent advances in large language models are pushing IoT systems toward autonomous Artificial Intelligence of Things (AIoT) paradigms, where system behavior depends on protocol semantics, internal state, interaction history, and environmental context. This creates a gap between AI-generated decisions and the behavior o...
Xiao-Yue Ma, Hua-Li Lu, Xiang-Xiang Dai et al.· 2026 IEEE/CIC International...· 0 citations
How artificial intelligence (AI), machine learning (ML), and generative AI (GenAI) enhance real-time threat detection, prediction, and response in distributed environments in distributed environments is explored, and the role of edge computing in decentralizing intelligence is highlighted, thereby reducing latency and...
Izabela Rojek, P. Kotlarz, D. Mikołajewski· Electronics· 0 citations
ADAE is introduced, a proposed AI engineering subdiscipline concerned with establishing measurable, continuous, and actionable accountability for deployed AI systems and treats accountability as a deployment-layer property rather than solely as a property of an individual model.
It is asserted that trustworthiness is a systems property, not a single algorithmic feature, and that realising it requires coordinated advances in explainability, uncertainty quantification, human–AI interaction design, failure detection, and domain-specific data governance.
It is found that traditional risk assessment and testing approaches are insufficient for AI-powered CPS, and a prototype implementation and experimental evaluation are presented along with a case study of protecting a smart manufacturing plant during a ransomware attack using the proposed approach.
Vikram Kulothungan, Deepti Gupta, Raju Dhakal et al.· Italian National Conference...· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
MIT News · Artificial Intelligence· news.mit.eduJul 7, 2026
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
